In [222]:
# Define HOG Feature Extraction Function #
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import cv2
import glob
from skimage.feature import hog
%matplotlib inline
import time
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split


def get_hog_features(img, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=True):
    if vis == True:
        features, hog_image = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),
                                  cells_per_block=(cell_per_block, cell_per_block), transform_sqrt=False, 
                                  visualise=True, feature_vector=False,block_norm='L2-Hys')
        return features, hog_image
    else:      
        features = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),
                       cells_per_block=(cell_per_block, cell_per_block), transform_sqrt=False, 
                       visualise=False, feature_vector=feature_vec,block_norm='L2-Hys')
    return features
print ('#')
#
In [223]:
##Use Training Images
In [224]:
# Read in our vehicles and non-vehicles
car_images = glob.glob('training_data/vehicles/**/*.png', recursive=True)
notcar_images=glob.glob('training_data/non-vehicles/**/*.png', recursive=True)
cars = []
notcars = []

for image in car_images:
        cars.append(image)
for image in notcar_images:
        notcars.append(image)

print (len(cars))
print (len(notcars))
8792
8968
In [225]:
####See Some Imported Dataset###

fig, axs = plt.subplots(8,8, figsize=(16, 16))
fig.subplots_adjust(hspace = .2, wspace=.001)
axs = axs.ravel()

# Step through the list and search for chessboard corners
for i in np.arange(32):
    img = cv2.imread(cars[np.random.randint(0,len(cars))])
    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    axs[i].axis('off')
    axs[i].set_title('Car', fontsize=10)
    axs[i].imshow(img)
for i in np.arange(32,64):
    img = cv2.imread(notcars[np.random.randint(0,len(notcars))])
    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    axs[i].axis('off')
    axs[i].set_title('Not Car', fontsize=10)
    axs[i].imshow(img)
In [226]:
# Generate a random index to look at a car image
carind = np.random.randint(0, len(cars))
notcarind = np.random.randint(0, len(notcars))
# Read in the image
carimage = cv2.imread(cars[carind])
cargray = cv2.cvtColor(carimage, cv2.COLOR_BGR2GRAY)
notcarimage = cv2.imread(notcars[notcarind])
notcargray = cv2.cvtColor(notcarimage, cv2.COLOR_BGR2GRAY)
# Define HOG parameters
orient = 9
pix_per_cell = 8
cell_per_block = 2
# Call our function with vis=True to see an image output
features, hog_image = get_hog_features(cargray, orient, 
                        pix_per_cell, cell_per_block, 
                        vis=True, feature_vec=False)


features, hog_image2 = get_hog_features(notcargray, orient, 
                        pix_per_cell, cell_per_block, 
                        vis=True, feature_vec=False)
# Plot the examples
fig = plt.figure()
fig.subplots_adjust(hspace = .5, wspace=.001)
plt.subplot(221)
plt.imshow(carimage, cmap='gray')
plt.title('Example Car Image')
plt.subplot(222)
plt.imshow(hog_image, cmap='gray')
plt.title('HOG Visualization')
plt.subplot(223)
plt.imshow(notcarimage, cmap='gray')
plt.title('Example Not Car Image')
plt.subplot(224)
plt.imshow(hog_image2, cmap='gray')
plt.title('HOG Visualization')
Out[226]:
<matplotlib.text.Text at 0x118e22ba8>
In [227]:
# Define a function to compute binned color features  
def bin_spatial(img, size=(32, 32)):
    # Use cv2.resize().ravel() to create the feature vector
    features = cv2.resize(img, size).ravel() 
    # Return the feature vector
#     print ('Spatial Feature Shape:')
#     print (features.shape)
    return features

# Define a function to compute color histogram features  
def color_hist(img, nbins=32, bins_range=(0, 256)):
    # Compute the histogram of the color channels separately
    channel1_hist = np.histogram(img[:,:,0], bins=nbins, range=bins_range)
    channel2_hist = np.histogram(img[:,:,1], bins=nbins, range=bins_range)
    channel3_hist = np.histogram(img[:,:,2], bins=nbins, range=bins_range)
    # Concatenate the histograms into a single feature vector
    hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
    # Return the individual histograms, bin_centers and feature vector
#     print ('Hist Feature Shape:')
#     print (hist_features.shape)
    return hist_features

Method to Extract HOG Features on Image Set

In [228]:
# Define a function to extract features from a list of images
# Have this function call bin_spatial() and color_hist()
def extract_features(imgs, cspace='RGB', orient=9, 
                        pix_per_cell=8, cell_per_block=2, hog_channel=0,spatial_size=(32, 32),
                        hist_bins=32, hist_range=(0, 256)):
    # Create a list to append feature vectors to
    features = []
    # Iterate through the list of images
    for file in imgs:
        # Read in each one by one
        image = mpimg.imread(file)
        #print (image.shape)
        # apply color conversion if other than 'RGB'
        if cspace != 'RGB':
            if cspace == 'HSV':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
            elif cspace == 'LUV':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)
            elif cspace == 'HLS':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
            elif cspace == 'YUV':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
            elif cspace == 'YCrCb':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
        else: feature_image = np.copy(image)      
        # Call get_hog_features() with vis=False, feature_vec=True
        if hog_channel == 'ALL':
            hog_features = []
            for channel in range(feature_image.shape[2]):
                hog_features.append(get_hog_features(feature_image[:,:,channel], 
                                    orient, pix_per_cell, cell_per_block, 
                                    vis=False, feature_vec=True))
            hog_features = np.ravel(hog_features)        
        else:
            hog_features = get_hog_features(feature_image[:,:,hog_channel], orient, 
                        pix_per_cell, cell_per_block, vis=False, feature_vec=True)
        # Apply bin_spatial() to get spatial color features
        spatial_features = bin_spatial(feature_image, size=spatial_size)
        # Apply color_hist() also with a color space option now
        hist_features = color_hist(feature_image, nbins=hist_bins, bins_range=hist_range)
        # Append the new feature vector to the features list
        features.append(np.concatenate((hog_features,spatial_features, hist_features)))
    # Return list of feature vectors
    return features
print('#')
#

Test the Extract Features on a Set

In [326]:
### TODO: Tweak these parameters and see how the results change.
colorspace = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 11
pix_per_cell = 16
cell_per_block = 2
hog_channel = "ALL" # Can be 0, 1, 2, or "ALL"
spatial = 32
histbin = 32
# sample_size = 500
# cars = cars[0:sample_size]
# notcars = notcars[0:sample_size]

t=time.time()
car_features = extract_features(cars, cspace=colorspace, orient=orient, 
                        pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, 
                        hog_channel=hog_channel,spatial_size=(spatial,spatial),hist_bins=histbin)
notcar_features = extract_features(notcars, cspace=colorspace, orient=orient, 
                        pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, 
                        hog_channel=hog_channel,spatial_size=(spatial,spatial),hist_bins=histbin)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to extract HOG features...')
# Create an array stack of feature vectors
X = np.vstack((car_features, notcar_features)).astype(np.float64)                        
# Fit a per-column scaler
X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
scaled_X = X_scaler.transform(X)

# Define the labels vector
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))


# Split up data into randomized training and test sets
rand_state = np.random.randint(0, 100)
X_train, X_test, y_train, y_test = train_test_split(
    scaled_X, y, test_size=0.2, random_state=rand_state)

print('Using:',orient,'orientations',pix_per_cell,
    'pixels per cell and', cell_per_block,'cells per block')
print('Feature vector length:', len(X_train[0]))
# Use a linear SVC 
svc = LinearSVC()
# Check the training time for the SVC
t=time.time()
svc.fit(X_train, y_train)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train SVC...')
# Check the score of the SVC
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
# Check the prediction time for a single sample
t=time.time()
n_predict = 10
print('My SVC predicts: ', svc.predict(X_test[0:n_predict]))
print('For these',n_predict, 'labels: ', y_test[0:n_predict])
t2 = time.time()
print(round(t2-t, 5), 'Seconds to predict', n_predict,'labels with SVC')
136.76 Seconds to extract HOG features...
Using: 11 orientations 16 pixels per cell and 2 cells per block
Feature vector length: 4356
19.2 Seconds to train SVC...
Test Accuracy of SVC =  0.9752
My SVC predicts:  [ 1.  0.  1.  1.  0.  0.  1.  0.  1.  1.]
For these 10 labels:  [ 1.  0.  1.  1.  0.  0.  1.  0.  1.  1.]
0.00308 Seconds to predict 10 labels with SVC
HOG Extraction SVC Training
Configuration Label Colorspace Orientations Pixels Per Cell Cells Per Block HOG Channel Extract Time SVC Training Test Accuracy of SVC
1 YCrCb 9 8 2 ALL 11.46 1.49 0.945
2 RGB 9 8 2 ALL 8.18 4.53 0.92
3 YCrCb 9 16 2 ALL 3.89 0.5 0.94
4 HSV 9 8 2 ALL 9.03 0.4 0.955
5 HLS 9 8 2 ALL 9.56 1.1 0.955
6 HLS 11 8 2 ALL 9.59 1.29 0.96
7 YUV 11 8 2 ALL 9.12 1.44 0.95
8 HLS 11 16 2 ALL 4.14 0.59 0.97

Method for Using Classifier to Detect Cars in an Image

Also Added Spatial Bin and Histogram Classification for SVC Prediction

In [327]:
# Define a single function that can extract features using hog sub-sampling and make predictions
def find_cars(img, ystart, ystop, scale, cspace, hog_channel, svc, X_scaler, orient, 
              pix_per_cell, cell_per_block, spatial_size, hist_bins, show_all_rectangles=False):
    
    # array of rectangles where cars were detected
    rectangles = []
    img_features = []
    img = img.astype(np.float32)/255
    
    img_tosearch = img[ystart:ystop,:,:]

    # apply color conversion if other than 'RGB'
    if cspace != 'RGB':
        if cspace == 'HSV':
            ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2HSV)
        elif cspace == 'LUV':
            ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2LUV)
        elif cspace == 'HLS':
            ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2HLS)
        elif cspace == 'YUV':
            ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YUV)
        elif cspace == 'YCrCb':
            ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YCrCb)
    else: ctrans_tosearch = np.copy(image)   
    
    # rescale image if other than 1.0 scale
    if scale != 1:
        imshape = ctrans_tosearch.shape
        ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
    
    # select colorspace channel for HOG 
    if hog_channel == 'ALL':
        ch1 = ctrans_tosearch[:,:,0]
        ch2 = ctrans_tosearch[:,:,1]
        ch3 = ctrans_tosearch[:,:,2]
    else: 
        ch1 = ctrans_tosearch[:,:,hog_channel]

    # Define blocks and steps as above
    nxblocks = (ch1.shape[1] // pix_per_cell)+1  #-1
    nyblocks = (ch1.shape[0] // pix_per_cell)+1  #-1 
    nfeat_per_block = orient*cell_per_block**2
    # 64 was the orginal sampling rate, with 8 cells and 8 pix per cell
    window = 64
    nblocks_per_window = (window // pix_per_cell)-1 
    cells_per_step = 2  # Instead of overlap, define how many cells to step
    nxsteps = (nxblocks - nblocks_per_window) // cells_per_step
    nysteps = (nyblocks - nblocks_per_window) // cells_per_step
    
    # Compute individual channel HOG features for the entire image
    hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)   
    if hog_channel == 'ALL':
        hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
        hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)
    
    for xb in range(nxsteps):
        for yb in range(nysteps):
            ypos = yb*cells_per_step
            xpos = xb*cells_per_step
            # Extract HOG for this patch
            hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
            if hog_channel == 'ALL':
                hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() 
                hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() 
                hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))
            else:
                hog_features = hog_feat1

            xleft = xpos*pix_per_cell
            ytop = ypos*pix_per_cell
            
            #img_features.append(hog_features)
            ################ ONLY FOR BIN_SPATIAL AND COLOR_HIST ################

            # Extract the image patch
            subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64,64))
          
            # Get color features
            spatial_features = bin_spatial(subimg, size=spatial_size)
            #img_features.append(spatial_features)
            hist_features = color_hist(subimg, nbins=hist_bins)
            #img_features.append(hist_features)
            join_features = np.hstack((hog_features, spatial_features, hist_features))
            #join_features = np.concatenate(img_features)
#             print(hog_features.shape)
#             print(spatial_features.shape)
#             print(hist_features.shape)
#             print(join_features.shape)
            # Scale features and make a prediction
            test_features = X_scaler.transform((join_features).reshape(1, -1))    
#             test_features = X_scaler.transform(np.hstack((shape_feat, hist_feat)).reshape(1, -1))    
            test_prediction = svc.predict(test_features)
            
            ######################################################################
            
#             test_prediction = svc.predict(hog_features)
            
            if test_prediction == 1 or show_all_rectangles:
                xbox_left = np.int(xleft*scale)
                ytop_draw = np.int(ytop*scale)
                win_draw = np.int(window*scale)
                rectangles.append(((xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart)))
                
    return rectangles
    
print('#')
#

Test Detection on Example Image

In [328]:
test_img = mpimg.imread('./test_images/test1.jpg')

ystart = 400
ystop = 656
scale = 1.5
colorspace = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 11
pix_per_cell = 16
cell_per_block = 2
hog_channel = 'ALL' # Can be 0, 1, 2, or "ALL"
spatial = 32
histbin = 32

rectangles = find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin)

print(len(rectangles), 'rectangles found in image')
3 rectangles found in image

Method to Draw Rectangles onto an Image

In [329]:
# Here is your draw_boxes function from the previous exercise
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
    # Make a copy of the image
    imcopy = np.copy(img)
    random_color = False
    # Iterate through the bounding boxes
    for bbox in bboxes:
        if color == 'random' or random_color:
            color = (np.random.randint(0,255), np.random.randint(0,255), np.random.randint(0,255))
            random_color = True
        # Draw a rectangle given bbox coordinates
        cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
    # Return the image copy with boxes drawn
    return imcopy

print('#')
#
In [330]:
test_img_rects = draw_boxes(test_img, rectangles)
plt.figure(figsize=(10,10))
plt.imshow(test_img_rects)
print('#')
#

Search Areas and Relative Scales

We will try scaling windows in different regions of image depending on distance from the camera.

In [331]:
test_img = mpimg.imread('./test_images/test1.jpg')
colorspace = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb


rects = []

ystart = 400
ystop = 464
scale = 1.0
rects.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin,show_all_rectangles=True))
ystart = 416
ystop = 480
scale = 1.0
rects.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin,show_all_rectangles=True))

rectangles = [item for sublist in rects for item in sublist] 
test_img_rects = draw_boxes(test_img, rectangles, color='random', thick=2)
plt.figure(figsize=(10,10))
plt.imshow(test_img_rects)
print('Number of boxes: ', len(rectangles))
Number of boxes:  78
In [332]:
test_img = mpimg.imread('./test_images/test1.jpg')
colorspace = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb


rects = []

ystart = 400
ystop = 496
scale = 1.5
rects.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin,show_all_rectangles=True))
ystart = 432
ystop = 528
scale = 1.5
rects.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin,show_all_rectangles=True))

rectangles = [item for sublist in rects for item in sublist] 
test_img_rects = draw_boxes(test_img, rectangles, color='random', thick=2)
plt.figure(figsize=(10,10))
plt.imshow(test_img_rects)
print('Number of boxes: ', len(rectangles))
Number of boxes:  50
In [333]:
test_img = mpimg.imread('./test_images/test1.jpg')
colorspace = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb


rects = []

ystart = 400
ystop = 528
scale = 2.0
rects.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin,show_all_rectangles=True))
ystart = 432
ystop = 560
scale = 2.0
rects.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin,show_all_rectangles=True))

rectangles = [item for sublist in rects for item in sublist] 
test_img_rects = draw_boxes(test_img, rectangles, color='random', thick=2)
plt.figure(figsize=(10,10))
plt.imshow(test_img_rects)
print('Number of boxes: ', len(rectangles))
Number of boxes:  38
In [334]:
test_img = mpimg.imread('./test_images/test1.jpg')
colorspace = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb


rects = []

ystart = 400
ystop = 596
scale = 3.0
rects.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin,show_all_rectangles=True))
ystart = 464
ystop = 660
scale = 3.0
rects.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin,show_all_rectangles=True))

rectangles = [item for sublist in rects for item in sublist] 
test_img_rects = draw_boxes(test_img, rectangles, color='random', thick=2)
plt.figure(figsize=(10,10))
plt.imshow(test_img_rects)
print('Number of boxes: ', len(rectangles))
Number of boxes:  24

Combine All Searches

In [335]:
test_img = mpimg.imread('./test_images/test1.jpg')

rectangles = []

colorspace = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 11
pix_per_cell = 16
cell_per_block = 2
hog_channel = "ALL" # Can be 0, 1, 2, or "ALL"


ystart = 400
ystop = 464
scale = 1.0
rectangles.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin))
ystart = 416
ystop = 480
scale = 1.0
rectangles.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin))


ystart = 400
ystop = 496
scale = 1.5
rectangles.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin))


ystart = 432
ystop = 528
scale = 1.5
rectangles.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin))


ystart = 400
ystop = 528
scale = 2.0
rectangles.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin))


ystart = 432
ystop = 560
scale = 2.0
rectangles.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin))


ystart = 400
ystop = 596
scale = 3.5
rectangles.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin))


ystart = 464
ystop = 660
scale = 3.5
rectangles.append(find_cars(test_img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin))



# apparently this is the best way to flatten a list of lists
rectangles = [item for sublist in rectangles for item in sublist] 
test_img_rects = draw_boxes(test_img, rectangles, color='random', thick=2)
plt.figure(figsize=(10,10))
plt.imshow(test_img_rects)
print('#')
#

Heatmap

This method (duplicated from lesson material) produces a heat map based on rectangle locations (additive with overlap).

In [336]:
def add_heat(heatmap, bbox_list):
    # Iterate through list of bboxes
    for box in bbox_list:
        # Add += 1 for all pixels inside each bbox
        # Assuming each "box" takes the form ((x1, y1), (x2, y2))
        heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1

    # Return updated heatmap
    return heatmap

print('#')
#
In [337]:
# Test out the heatmap
heatmap_img = np.zeros_like(test_img[:,:,0])
heatmap_img = add_heat(heatmap_img, rectangles)
plt.figure(figsize=(10,10))
plt.imshow(heatmap_img, cmap='hot')

print('#')
#

Heatmap Thresholding

In [338]:
def apply_threshold(heatmap, threshold):
    # Zero out pixels below the threshold
    heatmap[heatmap <= threshold] = 0
    # Return thresholded map
    return heatmap

print('#')
#
In [340]:
heatmap_img = apply_threshold(heatmap_img, 2)
plt.figure(figsize=(10,10))
plt.imshow(heatmap_img, cmap='hot')

print('...')
...

Labels to Heatmap

In [341]:
from scipy.ndimage.measurements import label

labels = label(heatmap_img)
plt.figure(figsize=(10,10))
plt.imshow(labels[0], cmap='gray')
print(labels[1], 'cars found')
2 cars found

Draw Bounding Boxes for Lables

In [342]:
def draw_labeled_bboxes(img, labels):
    # Iterate through all detected cars
    rects = []
    for car_number in range(1, labels[1]+1):
        # Find pixels with each car_number label value
        nonzero = (labels[0] == car_number).nonzero()
        # Identify x and y values of those pixels
        nonzeroy = np.array(nonzero[0])
        nonzerox = np.array(nonzero[1])
        # Define a bounding box based on min/max x and y
        bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
        rects.append(bbox)
        # Draw the box on the image
        cv2.rectangle(img, bbox[0], bbox[1], (0,0,255), 6)
    # Return the image and final rectangles
    return img, rects

# Draw bounding boxes on a copy of the image
draw_img, rect = draw_labeled_bboxes(np.copy(test_img), labels)
# Display the image
plt.figure(figsize=(10,10))
plt.imshow(draw_img)
print('#')
#

Process Image Pipeline

In [343]:
def process_frame(img):

    rectangles = []

    colorspace = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
    orient = 11
    pix_per_cell = 16
    cell_per_block = 2
    hog_channel = 'ALL' # Can be 0, 1, 2, or "ALL"

    ystart = 400
    ystop = 464
    scale = 1.0
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin))
    ystart = 416
    ystop = 480
    scale = 1.0
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin))
    ystart = 400
    ystop = 496
    scale = 1.5
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin))
    ystart = 432
    ystop = 528
    scale = 1.5
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin))
    ystart = 400
    ystop = 528
    scale = 2.0
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin))
    ystart = 432
    ystop = 560
    scale = 2.0
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin))
    ystart = 400
    ystop = 596
    scale = 3.5
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin))
    ystart = 464
    ystop = 660
    scale = 3.5
    rectangles.append(find_cars(img, ystart, ystop, scale, colorspace, hog_channel, svc, X_scaler, orient, pix_per_cell, cell_per_block, (spatial,spatial), histbin))

    rectangles = [item for sublist in rectangles for item in sublist] 
    
    heatmap_img = np.zeros_like(img[:,:,0])
    heatmap_img = add_heat(heatmap_img, rectangles)
    heatmap_img = apply_threshold(heatmap_img, 1)
    labels = label(heatmap_img)
    draw_img, rects = draw_labeled_bboxes(np.copy(img), labels)
    return draw_img

print('#')
#
In [344]:
test_images = glob.glob('./test_images/test*.jpg')

fig, axs = plt.subplots(3, 2, figsize=(16,14))
fig.subplots_adjust(hspace = .004, wspace=.002)
axs = axs.ravel()

for i, im in enumerate(test_images):
    axs[i].imshow(process_frame(mpimg.imread(im)))
    axs[i].axis('off')

Test Pipeline on Video

In [345]:
from moviepy.editor import VideoFileClip

test_out_file = 'test_video_out.mp4'
clip_test = VideoFileClip('test_video.mp4')
clip_test_out = clip_test.fl_image(process_frame)
%time clip_test_out.write_videofile(test_out_file, audio=False)
[MoviePy] >>>> Building video test_video_out.mp4
[MoviePy] Writing video test_video_out.mp4
 97%|█████████▋| 38/39 [00:22<00:00,  1.79it/s]
[MoviePy] Done.
[MoviePy] >>>> Video ready: test_video_out.mp4 

CPU times: user 19.9 s, sys: 1.5 s, total: 21.4 s
Wall time: 24.2 s
In [ ]: